
@article{ZGTJ202303004,
	author = {国家统计局},
	title = {中华人民共和国2022年国民经济和社会发展统计公报},
	journal = {中国统计},
	volume = {},
	number = {12-29},
	year = {2023},
}  

@inproceedings{wei2018intellilight,
	title={Intellilight: A reinforcement learning approach for intelligent traffic light control},
	author={Wei, Hua and Zheng, Guanjie and Yao, Huaxiu and Li, Zhenhui},
	booktitle={Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery \& data mining},
	pages={2496--2505},
	year={2018}
}

@inproceedings{zheng2019learning,
	title={Learning phase competition for traffic signal control},
	author={Zheng, Guanjie and Xiong, Yuanhao and Zang, Xinshi and Feng, Jie and Wei, Hua and Zhang, Huichu and Li, Yong and Xu, Kai and Li, Zhenhui},
	booktitle={Proceedings of the 28th ACM international conference on information and knowledge management},
	pages={1963--1972},
	year={2019}
}

@article{zhang2022dynamiclight,
	title={DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL},
	author={Zhang, Liang and Wu, Qiang and Shen, Jun and L{\"u}, Linyuan and Du, Bo and Telikani, Akbar and Wu, Jianqing and Xie, Shubin},
	journal={arXiv preprint arXiv:2211.01025},
	year={2022}
}


@article{lu2023dualight,
	title={DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge},
	author={Lu, Jiaming and Ruan, Jingqing and Jiang, Haoyuan and Li, Ziyue and Mao, Hangyu and Zhao, Rui},
	journal={arXiv preprint arXiv:2312.14532},
	year={2023}
}

%Introduction
@inproceedings{jiang2021dl,
  title={Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction},
  author={Jiang, Renhe and Yin, Du and Wang, Zhaonan and Wang, Yizhuo and Deng, Jiewen and Liu, Hangchen and Cai, Zekun and Deng, Jinliang and Song, Xuan and Shibasaki, Ryosuke},
  booktitle={Proceedings of the 30th ACM international conference on information \& knowledge management},
  pages={4515--4525},
  year={2021}
}

@inproceedings{ijcai2018p505,
  title     = {Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting},
  author    = {Bing Yu and Haoteng Yin and Zhanxing Zhu},
  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on
               Artificial Intelligence, {IJCAI-18}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages     = {3634--3640},
  year      = {2018},
  month     = {7},
  doi       = {10.24963/ijcai.2018/505},
  url       = {https://doi.org/10.24963/ijcai.2018/505},
}





@INPROCEEDINGS{You2022ROLAND,
  author={You, J. and Du, T. and Leskovec, J.},
  booktitle={Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '22)}, 
  title={ROLAND: Graph Learning Framework for Dynamic Graphs}, 
  year={2022},
  volume={},
  number={},
  pages={10},
  keywords={Graph Neural Networks; Dynamic Graphs; Network Analysis},
  doi={10.1145/3534678.3539300}
}

@INPROCEEDINGS{peng2021dynamic,
  author={Peng, H. and Du, B. and Liu, M. and Liu, M. and Ji, S. and Wang, S. and Zhang, X. and He, L.},
  booktitle={IEEE International Conference on Computer Vision (ICCV), 2021}, 
  title={Dynamic Graph Convolutional Network for Long-Term Traffic Flow Prediction with Reinforcement Learning}, 
  year={2021},
  volume={},
  number={},
  pages={9},
  keywords={Traffic Flow Prediction, Dynamic Graph, Graph Convolutional Policy Network, Spatio-Temporal Prediction, Reinforcement Learning},
  doi={10.1109/IVS.2004.1336475}
}



@INPROCEEDINGS{9208032,
  author={Makhmutova, Alisa and Anikin, Igor V. and Dagaeva, Maria},
  booktitle={2020 International Russian Automation Conference (RusAutoCon)}, 
  title={Object Tracking Method for Videomonitoring in Intelligent Transport Systems}, 
  year={2020},
  volume={},
  number={},
  pages={535-540},
  keywords={Roads;Object detection;Object tracking;Streaming media;Feature extraction;Urban areas;computer vision;tracking;transport systems;video processing},
  doi={10.1109/RusAutoCon49822.2020.9208032}}


@INPROCEEDINGS{1336475,
  author={Mobus, R. and Kolbe, U.},
  booktitle={IEEE Intelligent Vehicles Symposium, 2004}, 
  title={Multi-target multi-object tracking, sensor fusion of radar and infrared}, 
  year={2004},
  volume={},
  number={},
  pages={732-737},
  keywords={Radar tracking;Sensor fusion;Infrared sensors;Infrared detectors;Road safety;Radar detection;Object detection;Sensor systems;Filtering algorithms;System testing},
  doi={10.1109/IVS.2004.1336475}}


@INPROCEEDINGS{10688174,
  author={Ma, Jianbo and Tang, Chuanming and Wu, Fei and Zhao, Can and Zhang, Jianlin and Xu, Zhiyong},
  booktitle={2024 IEEE International Conference on Multimedia and Expo (ICME)}, 
  title={STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking}, 
  year={2024},
  volume={},
  number={},
  pages={1-6},
  keywords={Deformable models;Target tracking;Source coding;Object detection;Feature extraction;Boosting;Autonomous aerial vehicles;Multiple Object Tracking;Spatio-Temporal Model;Unmanned Aerial Vehicle},
  doi={10.1109/ICME57554.2024.10688174}}

@ARTICLE{9349962,
  author={Lin, Lei and Li, Weizi and Bi, Huikun and Qin, Lingqiao},
  journal={IEEE Intelligent Transportation Systems Magazine}, 
  title={Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms}, 
  year={2022},
  volume={14},
  number={2},
  pages={197-208},
  keywords={Trajectory;Predictive models;Autonomous vehicles;Data models;Vehicle dynamics;Training data;Spatiotemporal phenomena},
  doi={10.1109/MITS.2021.3049404}}

@ARTICLE{9764653,
  author={Yang, Shuo and Lu, Huimin and Li, Jianru},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={Multifeature Fusion-Based Object Detection for Intelligent Transportation Systems}, 
  year={2023},
  volume={24},
  number={1},
  pages={1126-1133},
  keywords={Feature extraction;Point cloud compression;Three-dimensional displays;Object detection;Task analysis;Intelligent transportation systems;Tensors;3D object detection;point clouds;feature fusion},
  doi={10.1109/TITS.2022.3155488}}

@ARTICLE{9224629,
  author={Jimoh, Onemayin David and Ajao, Lukman Adewale and Adeleke, Oluwafemi Oyetunde and Kolo, Stephen Sunday},
  journal={IEEE Access}, 
  title={A Vehicle Tracking System Using Greedy Forwarding Algorithms for Public Transportation in Urban Arterial}, 
  year={2020},
  volume={8},
  number={},
  pages={191706-191725},
  keywords={Radar tracking;Global Positioning System;Public transportation;Real-time systems;Satellites;Urban areas;Arduino board;bus terminal;global positioning system;google map;longitude;pseudo-range;public transportation;Raspberry Pi;satellite;vehicle tracking system},
  doi={10.1109/ACCESS.2020.3031488}}

@InProceedings{Yan_2021_ICCV,
    author    = {Yan, Bin and Peng, Houwen and Fu, Jianlong and Wang, Dong and Lu, Huchuan},
    title     = {Learning Spatio-Temporal Transformer for Visual Tracking},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {10448-10457}
}

@INPROCEEDINGS{10447969,
  author={Jian, Yajun and Zhuang, Chihui and He, Wenyan and Du, Kaiwen and Lu, Yang and Wang, Hanzi},
  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Spatio-Temporal Correlation Learning for Multiple Object Tracking}, 
  year={2024},
  volume={},
  number={},
  pages={6170-6174},
  keywords={Location awareness;Correlation;Deformation;Estimation;Object detection;Signal processing;Transformers;Multi-object Tracking;Spatio-Temporal Correlation Learning;Instance-Aware Localization;Temporal Context Aggregation},
  doi={10.1109/ICASSP48485.2024.10447969}}




@inproceedings{seo2018structured,
  title={Structured sequence modeling with graph convolutional recurrent networks},
  author={Seo, Youngjoo and Defferrard, Micha{\"e}l and Vandergheynst, Pierre and Bresson, Xavier},
  booktitle={Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25},
  pages={362--373},
  year={2018},
  organization={Springer}
}


@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, A},
  journal={Advances in Neural Information Processing Systems},
  year={2017}
}

@article{cai2020traffic,
  title={Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting},
  author={Cai, Ling and Janowicz, Krzysztof and Mai, Gengchen and Yan, Bo and Zhu, Rui},
  journal={Transactions in GIS},
  volume={24},
  number={3},
  pages={736--755},
  year={2020},
  publisher={Wiley Online Library}
}


@article{wang2021inductive,
  title={Inductive representation learning in temporal networks via causal anonymous walks},
  author={Wang, Yanbang and Chang, Yen-Yu and Liu, Yunyu and Leskovec, Jure and Li, Pan},
  journal={arXiv preprint arXiv:2101.05974},
  year={2021}
}

@article{sato2019dyane,
  title={Dyane: dynamics-aware node embedding for temporal networks},
  author={Sato, Koya and Oka, Mizuki and Barrat, Alain and Cattuto, Ciro},
  journal={arXiv preprint arXiv:1909.05976},
  year={2019}
}

@inproceedings{trivedi2019dyrep,
  title={Dyrep: Learning representations over dynamic graphs},
  author={Trivedi, Rakshit and Farajtabar, Mehrdad and Biswal, Prasenjeet and Zha, Hongyuan},
  booktitle={International conference on learning representations},
  year={2019}
}

% Related Work
@inproceedings{dey2017gate,
  title={Gate-variants of gated recurrent unit (GRU) neural networks},
  author={Dey, Rahul and Salem, Fathi M},
  booktitle={2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS)},
  pages={1597--1600},
  year={2017},
  organization={IEEE}
}

@inproceedings{69e088c8129341ac89810907fe6b1bfe,
title = "Empirical evaluation of gated recurrent neural networks on sequence modeling",
author = "Junyoung Chung and Caglar Gulcehre and Kyunghyun Cho and Yoshua Bengio",
year = "2014",
language = "English (US)",
booktitle = "NIPS 2014 Workshop on Deep Learning, December 2014",
}


@inproceedings{10.5555/3304222.3304273,
author = {Yu, Bing and Yin, Haoteng and Zhu, Zhanxing},
title = {Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting},
year = {2018},
isbn = {9780999241127},
publisher = {AAAI Press},
abstract = {Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.},
booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence},
pages = {3634–3640},
numpages = {7},
location = {Stockholm, Sweden},
series = {IJCAI'18}
}




@inproceedings{li2018dcrnn_traffic,
  title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting},
  author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
  booktitle={International Conference on Learning Representations (ICLR '18)},
  year={2018}
}


@article{saravanan2019deep,
  title={A deep hybrid model for traffic flow prediction using CNN-rGRU},
  author={Saravanan, S and Venkatachalapathy, K},
  journal={Int. J. Anal. Exp. Modal Anal},
  volume={11},
  year={2019}
}


@article{zhao2019t,
  title={T-GCN: A temporal graph convolutional network for traffic prediction},
  author={Zhao, Ling and Song, Yujiao and Zhang, Chao and Liu, Yu and Wang, Pu and Lin, Tao and Deng, Min and Li, Haifeng},
  journal={IEEE transactions on intelligent transportation systems},
  volume={21},
  number={9},
  pages={3848--3858},
  year={2019},
  publisher={IEEE}
}

@inproceedings{jiang2023spatio,
  title={Spatio-temporal meta-graph learning for traffic forecasting},
  author={Jiang, Renhe and Wang, Zhaonan and Yong, Jiawei and Jeph, Puneet and Chen, Quanjun and Kobayashi, Yasumasa and Song, Xuan and Fukushima, Shintaro and Suzumura, Toyotaro},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={37},
  number={7},
  pages={8078--8086},
  year={2023}
}

@inproceedings{ijcai2019p264,
  title     = {Graph WaveNet for Deep Spatial-Temporal Graph Modeling},
  author    = {Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
               Artificial Intelligence, {IJCAI-19}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages     = {1907--1913},
  year      = {2019},
  month     = {7},
  doi       = {10.24963/ijcai.2019/264},
  url       = {https://doi.org/10.24963/ijcai.2019/264},
}


@INPROCEEDINGS{li2023dynamic,
  author={Li, F. and Feng, J. and Yan, H. and Jin, G. and Yang, F. and Sun, F. and Jin, D. and Li, Y.},
  booktitle={ACM Transactions on Knowledge Discovery from Data (TKDD), 2023}, 
  title={Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution}, 
  year={2023},
  volume={17},
  number={1},
  pages={9},
  keywords={Traffic prediction, dynamic graph construction, traffic benchmark},
  doi={10.1145/3532611}
}

@article{bai2020adaptive,
  title={Adaptive graph convolutional recurrent network for traffic forecasting},
  author={Bai, Lei and Yao, Lina and Li, Can and Wang, Xianzhi and Wang, Can},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={17804--17815},
  year={2020}
}

@inproceedings{ye2021coupled,
  title={Coupled layer-wise graph convolution for transportation demand prediction},
  author={Ye, Junchen and Sun, Leilei and Du, Bowen and Fu, Yanjie and Xiong, Hui},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={35},
  pages={4617--4625},
  year={2021}
}

@article{cao2020spectral,
  title={Spectral temporal graph neural network for multivariate time-series forecasting},
  author={Cao, Defu and Wang, Yujing and Duan, Juanyong and Zhang, Ce and Zhu, Xia and Huang, Congrui and Tong, Yunhai and Xu, Bixiong and Bai, Jing and Tong, Jie and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={17766--17778},
  year={2020}
}

@article{lablack2023spatio,
  title={Spatio-temporal graph mixformer for traffic forecasting},
  author={Lablack, Mourad and Shen, Yanming},
  journal={Expert systems with applications},
  volume={228},
  pages={120281},
  year={2023},
  publisher={Elsevier}
}


@INPROCEEDINGS{yule2023towards,
  author={Yu, L. and Sun, L. and Du, B. and Lv, W.},
  booktitle={37th Conference on Neural Information Processing Systems (NeurIPS 2023)}, 
  title={Towards Better Dynamic Graph Learning: New Architecture and Unified Library}, 
  year={2023},
  volume={},
  number={},
  pages={10},
  keywords={Dynamic Graph Learning; Transformer; Sensor Fusion; Infrared Sensors; Radar Detection},
  doi={10.1109/IVS.2004.1336475}
}


@article{hamilton1994autoregressive,
  title={Autoregressive conditional heteroskedasticity and changes in regime},
  author={Hamilton, James D and Susmel, Raul},
  journal={Journal of econometrics},
  volume={64},
  number={1-2},
  pages={307--333},
  year={1994},
  publisher={Elsevier}
}

@inproceedings{pan2012utilizing,
  title={Utilizing real-world transportation data for accurate traffic prediction},
  author={Pan, Bei and Demiryurek, Ugur and Shahabi, Cyrus},
  booktitle={2012 ieee 12th international conference on data mining},
  pages={595--604},
  year={2012},
  organization={IEEE}
}

@article{hochreiter1997long,
  title={Long Short-term Memory},
  author={Hochreiter, S},
  journal={Neural Computation MIT-Press},
  year={1997}
}

@inproceedings{cho-etal-2014-properties,
    title = "On the Properties of Neural Machine Translation: Encoder{--}Decoder Approaches",
    author = {Cho, Kyunghyun  and
      van Merri{\"e}nboer, Bart  and
      Bahdanau, Dzmitry  and
      Bengio, Yoshua},
    editor = "Wu, Dekai  and
      Carpuat, Marine  and
      Carreras, Xavier  and
      Vecchi, Eva Maria",
    booktitle = "Proceedings of {SSST}-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = oct,
    year = "2014",
    address = "Doha, Qatar",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W14-4012",
    doi = "10.3115/v1/W14-4012",
    pages = "103--111",
}

@inproceedings{song2020spatial,
  title={Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting},
  author={Song, Chao and Lin, Youfang and Guo, Shengnan and Wan, Huaiyu},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={34},
  pages={914--921},
  year={2020}
}

@ARTICLE{10726722,
  author={Shao, Zezhi and Wang, Fei and Xu, Yongjun and Wei, Wei and Yu, Chengqing and Zhang, Zhao and Yao, Di and Sun, Tao and Jin, Guangyin and Cao, Xin and Cong, Gao and Jensen, Christian S. and Cheng, Xueqi},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis}, 
  year={2024},
  volume={},
  number={},
  pages={1-14},
  keywords={Forecasting;Time series analysis;Benchmark testing;Transformers;Predictive models;Data models;Computer science;Reliability;Proposals;Electricity;Benchmarking;long-term time series forecasting;multivariate time series;spatial-temporal forecasting},
  doi={10.1109/TKDE.2024.3484454}}




 @inproceedings{liu2023staeformer,
  title={STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformers SOTA for Traffic Forecasting},
  author={Liu, Hangchen and Dong, Zheng and Jiang, Renhe and Deng, Jiewen and Chen, Q and Song, X},
  booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), Birmingham, UK},
  pages={21--25},
  year={2023}
}

@article{Park2020LearningMN,
  title={Learning Memory-Guided Normality for Anomaly Detection},
  author={Hyunjong Park and Jongyoun Noh and Bumsub Ham},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={14360-14369},
  url={https://api.semanticscholar.org/CorpusID:214713500}
}

@article{kipf2016semi,
  title={Semi-supervised classification with graph convolutional networks},
  author={Kipf, Thomas N and Welling, Max},
  journal={arXiv preprint arXiv:1609.02907},
  year={2016}
}

@article{Zheng_Fan_Wang_Qi_2020, title={GMAN: A Graph Multi-Attention Network for Traffic Prediction}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5477}, DOI={10.1609/aaai.v34i01.5477}, abstractNote={&lt;p&gt;Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.&lt;/p&gt;}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zheng, Chuanpan and Fan, Xiaoliang and Wang, Cheng and Qi, Jianzhong}, year={2020}, month={Apr.}, pages={1234-1241} }

@inproceedings{zhangAutomatedSpatioTemporalGraph2023b,
  title = {Automated {{Spatio-Temporal Graph Contrastive Learning}}},
  booktitle = {Proceedings of the {{ACM Web Conference}} 2023},
  author = {Zhang, Qianru and Huang, Chao and Xia, Lianghao and Wang, Zheng and Li, Zhonghang and Yiu, Siuming},
  year = {2023},
  month = apr,
  pages = {295--305},
  publisher = {ACM},
  address = {Austin TX USA},
  doi = {10.1145/3543507.3583304},
  urldate = {2024-11-14},
  abstract = {Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlations with graph neural networks. Despite their efectiveness, several key challenges have not been well addressed in existing methods: i) Data noise and missing are ubiquitous in many spatio-temporal scenarios due to a variety of factors. ii) Input spatio-temporal data (e.g., mobility traces) usually exhibits distribution heterogeneity across space and time. In such cases, current methods are vulnerable to the quality of the generated region graphs, which may lead to suboptimal performance. In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources. Our AutoST framework is built upon a heterogeneous graph neural architecture to capture the multi-view region dependencies with respect to POI semantics, mobility fow patterns and geographical positions. To improve the robustness of our GNN encoder against data noise and distribution issues, we design an automated spatio-temporal augmentation scheme with a parameterized contrastive view generator. AutoST can adapt to the spatio-temporal heterogeneous graph with multiview semantics well preserved. Extensive experiments for three downstream spatio-temporal mining tasks on several real-world datasets demonstrate the signifcant performance gain achieved by our AutoST over a variety of baselines. The code is publicly available at https://github.com/HKUDS/AutoST.},
  isbn = {978-1-4503-9416-1},
  langid = {english}
}

@article{zhangAdapGLAdaptiveGraph2022,
  title = {{{AdapGL}}: {{An}} Adaptive Graph Learning Algorithm for Traffic Prediction Based on Spatiotemporal Neural Networks},
  shorttitle = {{{AdapGL}}},
  author = {Zhang, Wei and Zhu, Fenghua and Lv, Yisheng and Tan, Chang and Liu, Wen and Zhang, Xin and Wang, Fei-Yue},
  date = {2022-06},
  journaltitle = {Transportation Research Part C: Emerging Technologies},
  shortjournal = {Transportation Research Part C: Emerging Technologies},
  volume = {139},
  pages = {103659},
  issn = {0968090X},
  doi = {10.1016/j.trc.2022.103659},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0968090X22001024},
  urldate = {2023-03-17},
  abstract = {With well-defined graphs, graph convolution based spatiotemporal neural networks for traffic prediction have achieved great performance in numerous tasks. Compared to other methods, the networks can exploit the latent spatial dependencies between nodes according to the adjacency relationship. However, as the topological structure of the real road network tends to be intricate, it is difficult to accurately quantify the correlations between nodes in advance. In this paper, we propose a graph convolutional network based adaptive graph learning algorithm (AdapGL) to acquire the complex dependencies. First, by developing a novel graph learning module, more possible correlations between nodes can be adaptively captured during training. Second, inspired by the expectation maximization (EM) algorithm, the parameters of the prediction network module and the graph learning module are optimized by alternate training. An elaborate loss function is leveraged for graph learning to ensure the sparsity of the generated affinity matrix. In this way, the expectation maximization of one part can be realized under the condition that the other part is the best estimate. Finally, the graph structure is updated by a weighted sum approach. The proposed algorithm can be applied to most graph convolution based networks for traffic forecast. Experimental results demonstrated that our method can not only further improve the accuracy of traffic prediction, but also effectively exploit the hidden correlations of the nodes. The source code is available at https: //github.com/goaheand/AdapGL-pytorch.},
  langid = {english}}
}

@article{DBLPrlcpc,
  author       = {A{\"{a}}ron van den Oord and
                  Yazhe Li and
                  Oriol Vinyals},
  title        = {Representation Learning with Contrastive Predictive Coding},
  journal      = {CoRR},
  volume       = {abs/1807.03748},
  year         = {2018},
  url          = {http://arxiv.org/abs/1807.03748},
  eprinttype    = {arXiv},
  eprint       = {1807.03748},
  timestamp    = {Mon, 13 Aug 2018 16:48:25 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1807-03748.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}


@article{10.14778/3551793.3551827,
author = {Shao, Zezhi and Zhang, Zhao and Wei, Wei and Wang, Fei and Xu, Yongjun and Cao, Xin and Jensen, Christian S.},
title = {Decoupled dynamic spatial-temporal graph neural network for traffic forecasting},
year = {2022},
issue_date = {July 2022},
publisher = {VLDB Endowment},
volume = {15},
number = {11},
issn = {2150-8097},
url = {https://doi.org/10.14778/3551793.3551827},
doi = {10.14778/3551793.3551827},
abstract = {We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task. Traffic data is often obtained from sensors deployed in a road network. Recent proposals on spatial-temporal graph neural networks have achieved great progress at modeling complex spatial-temporal correlations in traffic data, by modeling traffic data as a diffusion process. However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals. Unfortunately, nearly all previous works coarsely consider traffic signals entirely as the outcome of the diffusion, while neglecting the inherent signals, which impacts model performance negatively. To improve modeling performance, we propose a novel Decoupled Spatial-Temporal Framework (DSTF) that separates the diffusion and inherent traffic information in a data-driven manner, which encompasses a unique estimation gate and a residual decomposition mechanism. The separated signals can be handled subsequently by the diffusion and inherent modules separately. Further, we propose an instantiation of DSTF, Decoupled Dynamic Spatial-Temporal Graph Neural Network (D2STGNN), that captures spatial-temporal correlations and also features a dynamic graph learning module that targets the learning of the dynamic characteristics of traffic networks. Extensive experiments with four real-world traffic datasets demonstrate that the framework is capable of advancing the state-of-the-art.},
journal = {Proc. VLDB Endow.},
month = jul,
pages = {2733–2746},
numpages = {14}
}

@inproceedings{10.1145/3394486.3403118,
author = {Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Chang, Xiaojun and Zhang, Chengqi},
title = {Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks},
year = {2020},
isbn = {9781450379984},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3394486.3403118},
doi = {10.1145/3394486.3403118},
abstract = {Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.},
booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages = {753–763},
numpages = {11},
keywords = {graph neural networks, graph structure learning, multivariate time series forecasting, spatial-temporal graphs},
location = {Virtual Event, CA, USA},
series = {KDD '20}
}

@article{Zhang2024ResearchOT,
  title={Research on traffic flow forecasting based on interactive dynamic meta-graph learning},
  author={Hong Zhang and Siyu Zhu and Xijun Zhang and Lei Gong},
  journal={Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering},
  year={2024},
  url={https://api.semanticscholar.org/CorpusID:273718892}
}


@inproceedings{10.1609/aaai.v33i01.3301922,
author = {Guo, Shengnan and Lin, Youfang and Feng, Ning and Song, Chao and Wan, Huaiyu},
title = {Attention based spatial-temporal graph convolutional networks for traffic flow forecasting},
year = {2019},
isbn = {978-1-57735-809-1},
publisher = {AAAI Press},
url = {https://doi.org/10.1609/aaai.v33i01.3301922},
doi = {10.1609/aaai.v33i01.3301922},
abstract = {Forecasting the traffic flows is a critical issue for res and practitioners in the feld of transportation. Ho is very challenging since the traffic flows usually sh nonlinearities and complex patterns. Most existin flow prediction methods, lacking abilities of modelin namic spatial-temporal correlations of Traffic data, t not yield satisfactory prediction results. In this p propose a novel attention based spatial-temporal gr volutional network (ASTGCN) model to solve tra forecasting problem. ASTGCN mainly consists of dependent components to respectively model three ral properties of Traffic flows, i.e., recent, daily-peri weekly-periodic dependencies. More specifically, ea ponent contains two major parts: 1) the spatial-tem tention mechanism to effectively capture the dynami temporal correlations in Traffic data; 2) the spatial-t convolution which simultaneously employs graph tions to capture the spatial patterns and common convolutions to describe the temporal features. The o the three components are weighted fused to genera nal prediction results. Experiments on two real-world datasets from the Caltrans Performance Measurement System (PeMS) demonstrate that the proposed ASTGCN model outperforms the state-of-the-art baselines.},
booktitle = {Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence},
articleno = {114},
numpages = {8},
location = {Honolulu, Hawaii, USA},
series = {AAAI'19/IAAI'19/EAAI'19}
}

@article{qin_memory_2022,
	title = {Memory attention enhanced graph convolution long short‐term memory network for traffic forecasting},
	volume = {37},
	issn = {0884-8173, 1098-111X},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/int.22855},
	doi = {10.1002/int.22855},
	pages = {6555--6576},
	number = {9},
	journaltitle = {International Journal of Intelligent Systems},
	shortjournal = {Int J of Intelligent Sys},
	author = {Qin, Yanjun and Zhao, Fang and Fang, Yuchen and Luo, Haiyong and Wang, Chenxing},
	urldate = {2025-01-04},
	date = {2022-09},
	langid = {english},
}

@article{zhang_research_2024,
	title = {Research on Traffic Flow Forecasting Based on Dynamic Spatial-Temporal Transformer},
	volume = {2678},
	issn = {0361-1981, 2169-4052},
	url = {https://journals.sagepub.com/doi/10.1177/03611981231205880},
	doi = {10.1177/03611981231205880},
	abstract = {Accurate traffic flow forecasting is crucial for urban traffic control and route planning. Aiming at the difficulty in capturing dynamic spatio-temporal complexity of traffic flow, a dynamic spatio-temporal transformer ({DST}-Trans) model capable of modeling dynamic correlation of traffic flow is proposed, which consists of gated temporal convolutional network ({GTCN}), graph convolutional network ({GCN}), and spatio-temporal transformer ({ST}-{TF}). {GTCN} and {GCN} are utilized to capture the temporal and spatial characteristics of traffic flow, respectively. {ST}-{TF} includes a temporal transformer using temporal gated convolution and temporal multi-head self-attention to capture short-long term temporal features, and spatial transformer using spatial gated graph convolution and spatial multi-head self-attention to capture local-global dynamic spatial features. In addition, to take full advantage of the dynamic and static associations of road networks, multi-graph models of road relationship graph, similarity graph, and adaptive dynamic graph with {SGGC} are constructed. Experimental results show that the {DST}-Trans model in this paper shows good prediction performance in short-term (15 min), medium-term (30 min), and longterm (60 min) prediction, outperforming existing state-of-the-art models by up to approximately 7\%.},
	pages = {301--313},
	number = {7},
	journaltitle = {Transportation Research Record: Journal of the Transportation Research Board},
	shortjournal = {Transportation Research Record: Journal of the Transportation Research Board},
	author = {Zhang, Hong and Wang, Hongyan and Zhang, Xijun and Gong, Lei},
	urldate = {2025-01-04},
	date = {2024-07},
	langid = {english},
}





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@article{HPPO2024,
  author={Luo, Haoqing and Bie, Yiming and Jin, Sheng},
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  title={Reinforcement Learning for Traffic Signal Control in Hybrid Action Space}, 
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  keywords={Aerospace electronics;Optimization;Decision making;Transportation;Switches;Throughput;Regulation;Traffic signal control;deep reinforcement learning;proximal policy optimization;hybrid action space},
  doi={10.1109/TITS.2023.3344585}
}





